{"slug": "ai-changes-the-economics-of-software-rewrites", "title": "AI changes the economics of software rewrites", "summary": "AI changes the economics of software rewrites by favoring codebases with clear, common patterns over proprietary or inconsistent systems, as models produce higher-quality output when they already understand the patterns from training data. This gives popular tech stacks an AI advantage, making rewrites an opportunity to rebuild codebases around patterns that leverage AI strengths.", "body_md": "# AI slop starts with the codebase itself.\n\nAI changes rewrite economics because codebases with clear, common patterns get more leverage than proprietary or inconsistent systems.\n\nMy view on software rewrites has changed because of AI.\n\nThe quality of AI output isn't determined solely by your prompt.\n\nIt's determined by what the model already knows from training data, and the context you give it to work with.\n\nFor coding tasks, most of that context is the codebase.\n\nPopular tech stacks have an AI advantage because the model has seen millions of examples, including published sources.\n\nThe opposite is true for proprietary languages and private frameworks, with inconsistent patterns - these have to be taught - mostly using the limited context window available to models.\n\nCompare these two workflows:\n\n- Read the feature specification.\n- Read a codebase with clear, consistent, well-established patterns.\n- Generate the implementation.\n\nVersus:\n\n- Read the feature specification.\n- Read an inconsistent codebase with proprietary/legacy languages and historical baggage.\n- Read additional examples and documentation.\n- Generate the implementation.\n\nIn the first workflow, the codebase has established patterns the model easily understands. In the second, the model spends effort inferring them before it can solve the problem.\n\nMore context means more tokens, more prompting, more variance, and generally lower-quality output - aka cost.\n\nA rewrite isn't just an opportunity to modernise your technology stack - it's an opportunity to rebuild your codebase around clear, consistent patterns that play to AI's strengths instead of fighting them.\n\nYou could either be using AI to solve the problem, or you could spend the time trying to get AI to learn your language first.\n\nThat lost time is your competitors' advantage, and the gap is not just speed - it's output quality.\n\nI think this changes the economics of software rewrites.", "url": "https://wpnews.pro/news/ai-changes-the-economics-of-software-rewrites", "canonical_source": "https://thetruthasiseeitnow.com/ai-slop-starts-with-the-codebase-itself/", "published_at": "2026-07-09 05:46:50+00:00", "updated_at": "2026-07-09 06:12:50.612154+00:00", "lang": "en", "topics": ["artificial-intelligence", "developer-tools", "large-language-models"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/ai-changes-the-economics-of-software-rewrites", "markdown": "https://wpnews.pro/news/ai-changes-the-economics-of-software-rewrites.md", "text": "https://wpnews.pro/news/ai-changes-the-economics-of-software-rewrites.txt", "jsonld": "https://wpnews.pro/news/ai-changes-the-economics-of-software-rewrites.jsonld"}}